spykes

Almost any electrophysiology study of neural spiking data relies on a battery of standard analyses. Raster plots and peri-stimulus time histograms aligned to stimuli and behavior provide a snapshot visual description of neural activity. Similarly, tuning curves are the most standard way to characterize how neurons encode stimuli or behavioral preferences. With increasing popularity of population recordings, maximum-likelihood decoders based on tuning models are becoming part of this standard.

Yet, virtually every lab relies on a set of in-house analysis scripts to go from raw data to summaries. We want to improve this status quo in order to enable easier sharing, better reproducibility and fewer bugs.

Spykes is a collection of Python tools to make the visualization and analysis of neural data easy and reproducible.

For more, see the documentation.

Installation

Spykes can be installed using

pip install spykes

For more detailed installation options, see the documentation.

Authors

• Pavan Ramkumar
• Hugo Fernandes

Acknowledgments

• Konrad Kording for funding and support

Contents

• Getting Started
  • What is Spykes?
  • Installing
    • Vanilla
    • Bleeding-Edge
    • Local Version
  • Datasets
• Tutorials
  • Fitting Tuning Curves with Gradient Descent
    • Special Case 1: Poisson Generalized Linear Model (GLM)
    • Special Case 2: Generalized von Mises Model (GVM)
  • Minimizing Negative Log Likelihood with Gradient Descent
  • Decoding Feature from Population Activity
• Examples Gallery
• Contributing
  • Guidelines
  • Testing
  • Building Documentation

API

• Plotting
  • NeuroVis
  • PopVis
• Machine Learning
  • NeuroPop
  • STRF
  • Sparse Filtering
  • Poisson Layers
• Input / Output
  • Datasets
• Config
• Utils